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Change Management Part 1: The Big Picture

Dennis Drogseth

This is the first of a three-part series on change management. In this blog, I’ll try to answer the question, “What is change management?” from both a process and a benefits (or use-case) perspective.

In the second installment, I’ll address best practices for both planning for and measuring the success of change management initiatives. I’ll also examine some of the issues that EMA has seen arise when IT organizations try to establish a more cohesive cross-domain approach to managing change. In part three, I’ll focus on the impacts of cloud, agile, and mobile, including the growing need for investments in automation and analytics to make change management more effective.

Change Management Processes

Like many words and concepts in English language, especially when applied to technology, “change management” carries with it a wide variety of associations. In terms of the processes established in the IT Infrastructure Library (ITIL), change management is best understood as a strategic approach to planning for change.

ITIL defines change management succinctly as, “the process responsible for controlling the lifecycle of all changes, enabling beneficial changes to be made with minimum disruption to IT Services.” As such, change management is a logical system of governance that addresses a set of relevant questions, which include the following:

■ Who requested the change?

■ What is the reason for the change?

■ What is the desired result of the change?

■ What are the risks involved with making the change?

■ What resources are required to deliver the change?

■ Who is responsible for the build, test, and implementation of the change?

■ What is the relationship between this change and other changes?

But this system of governance doesn’t stand alone. Actually implementing and managing changes requires attention to other ITIL processes. These include (but are not limited to):

■ Service asset and configuration management (SACM) – “The process responsible for maintaining information about configuration items required to deliver an IT Service, including their relationships.” SACM addresses how IT hardware and software assets (including applications) have been configured and, even more critically, identifies the relationships and interdependencies affecting infrastructure and application assets.

■ Release and deployment management – “The process responsible for planning, scheduling and controlling the build, test and deployment of releases, and for delivering new functionality required by the business while protecting the integrity of existing services.” As you can imagine, release management and automation should go hand in hand.

There are other ITIL processes relevant to managing change effectively, including capacity management, problem management, availability management, and continual service improvement, just to name a few. From just this brief snapshot, you might get the (correct) impression that change management in the “big picture” is at the very heart of effective IT operations. If done correctly, change management touches all of IT—including the service desk, operational teams, development, the executive suite, and even non-IT service consumers. This central position makes change management both an opportunity and a challenge.

Change Management Use Cases

Image removed.Probably the best way to understand the “change management opportunity” is to look at some of the use cases affiliated with it. Effective change management can empower a wide range of other initiatives, from lifecycle asset management to DevOps, service impact management, and improved service performance. EMA consultants have estimated that more than 60% of IT service disruptions come from the impacts of changes made across the application infrastructure—and this estimate is conservative compared to some of the other industry estimates I’ve seen. Having good change management processes and technologies in place is also a foundation for better automation, as well as for better optimization of both public and private cloud resources. And the list goes on.

Even the list below, derived in large part from CMDB Systems: Making Change Work in the Age of Cloud and Agile, is a partial one, but it should provide a useful departure point for your planning—as you seek to prioritize the use case(s) most relevant to you.

■ Governance and compliance: Managing change to conform with critical industry, security, and asset-related requirements for compliance, while minimizing change-related disruptions. This, can provide significant financial benefits including OpEx savings, superior service availability, improved security and savings from avoiding the penalty costs incurred when changes are made poorly.

■ Data center consolidation—mergers and acquisitions: Planning new options for data center consolidation is definitely on the rise, and mergers and acquisitions often lead to data center consolidation initiatives. Effective change management can shorten consolidation time, minimize costs, and improve the quality of the outcome.

■ Disaster recovery – Disaster recovery initiatives may be an extension of data center consolidation, or they may be independent. Automating change for disaster recovery is one of the more common drivers for a more systemic approach to change management.

■ The proverbial “move to cloud” – The stunning rise of virtualization and the persistent move to assimilate both internal and public cloud options make change impact management and effective change automation essential.

■ Facilities management and Green IT – This use case requires dynamic insights into both configuration and “performance”-related attributes for configuration items (CIs), both internal to IT (servers, switches, desktops, etc.) and external to traditional IT boundaries (facilities, power, etc.).

■ Optimizing the end-user experience across heterogeneous endpoints – Meeting the challenges of unified endpoint management including mobile endpoints, requires a flexible adoption of change management best practices and automation. But the benefits of doing this can be significant—impacting asset management, security, and financial optimization, while increasing end-user satisfaction with IT services.

Change Management Part 2

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For fifteen years, observability lived downstream of everything else. Code shipped, something broke, an engineer went to the dashboards. The job was forensic. The pillars we built, such as logs, metrics, and traces, were designed for that role: tell a human what just happened, fast enough that they can make it stop. That role has quietly ended ...

Hybrid IT has become the standard operating model for enterprises — but that companies are still looking for the right hybrid IT mix, according to the 2026 State of the Data Center Report from CoreSite. After years of cloud migration and hybrid adoption, organizations are shifting their focus from deciding whether to use cloud, colocation or on-premises infrastructure to determining which workloads belong in each environment ...

Pilots are everywhere, stakeholders are seeking results, businesses are pushing for new tools, and IT teams are being asked to make AI secure, reliable, and useful at scale. But as organizations move from testing AI to operationalizing it, many are discovering that the biggest barrier is not the model, the use case, or even the budget. It is the file data foundation within ...

Fast or cheap? For most of my career in engineering, speed and quality sat on opposite ends of a seesaw. The "OR" in "fast or cheap" was non-negotiable. It was expected that pushing for faster releases meant that something in quality would give way. Tightening quality controls meant the schedule slipped. Every engineering leader I know has lived some version of that tradeoff ... The seesaw is starting to level out ...

I have been building enterprise software for more than 20 years ... One thing stays true across all of it: You do not find out your foundation is wrong during the crisis. You find out when the debt comes due. For a lot of organizations, that bill is arriving now. New research ... puts hard numbers on something practitioners have been sensing for a while. The telemetry problem isn't coming. It's already here ...

The rapid growth of AI workloads is pushing traditional log management approaches to their limits, according to The State of Log Management 2026 report from Dynatrace. Modern logs have become critical to understanding, validating, and securing AI-driven decisions, helping organizations ensure reliability, compliance, and performance at scale. However, the volume and complexity of AI telemetry are overwhelming legacy tools ...

For years, secure connectivity has relied on a familiar pattern: route traffic back to centralized gateways, inspect it, and then allow access. This model worked when applications lived in a handful of data centers and users were largely confined to offices. That model is now under strain. Applications are distributed across clouds, users connect from everywhere, and real-time workloads demand performance that centralized inspection points struggle to deliver. As traffic volumes grow and latency expectations shrink, routing everything through a small number of control points has become both a performance bottleneck and a resilience risk. The future of secure connectivity requires a different approach ...

The AI experimentation phase is over, and the private cloud is where enterprise AI workloads are being deployed for security and scale, according to Private Cloud Outlook 2026, a new report from Broadcom ... 2026 marks an acceleration into a full AI tipping point. The shift is being shaped by three forces — costs, complexity, and control — that public cloud environments are increasingly failing to address for production AI at scale. Key findings from the report include ...

44% of organizations have reported an outage in the past year tied to suppressed or ignored alerts, and 78% had at least one incident where no alert was fired at all ... Engineers learned about failures from customers. That gap between what our tools report and what our customers experience is the problem DevOps teams have been quietly solving with GenAI tooling, even as most enterprises continue to run their NOCs on manual alert triage ...

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

Change Management Part 1: The Big Picture

Dennis Drogseth

This is the first of a three-part series on change management. In this blog, I’ll try to answer the question, “What is change management?” from both a process and a benefits (or use-case) perspective.

In the second installment, I’ll address best practices for both planning for and measuring the success of change management initiatives. I’ll also examine some of the issues that EMA has seen arise when IT organizations try to establish a more cohesive cross-domain approach to managing change. In part three, I’ll focus on the impacts of cloud, agile, and mobile, including the growing need for investments in automation and analytics to make change management more effective.

Change Management Processes

Like many words and concepts in English language, especially when applied to technology, “change management” carries with it a wide variety of associations. In terms of the processes established in the IT Infrastructure Library (ITIL), change management is best understood as a strategic approach to planning for change.

ITIL defines change management succinctly as, “the process responsible for controlling the lifecycle of all changes, enabling beneficial changes to be made with minimum disruption to IT Services.” As such, change management is a logical system of governance that addresses a set of relevant questions, which include the following:

■ Who requested the change?

■ What is the reason for the change?

■ What is the desired result of the change?

■ What are the risks involved with making the change?

■ What resources are required to deliver the change?

■ Who is responsible for the build, test, and implementation of the change?

■ What is the relationship between this change and other changes?

But this system of governance doesn’t stand alone. Actually implementing and managing changes requires attention to other ITIL processes. These include (but are not limited to):

■ Service asset and configuration management (SACM) – “The process responsible for maintaining information about configuration items required to deliver an IT Service, including their relationships.” SACM addresses how IT hardware and software assets (including applications) have been configured and, even more critically, identifies the relationships and interdependencies affecting infrastructure and application assets.

■ Release and deployment management – “The process responsible for planning, scheduling and controlling the build, test and deployment of releases, and for delivering new functionality required by the business while protecting the integrity of existing services.” As you can imagine, release management and automation should go hand in hand.

There are other ITIL processes relevant to managing change effectively, including capacity management, problem management, availability management, and continual service improvement, just to name a few. From just this brief snapshot, you might get the (correct) impression that change management in the “big picture” is at the very heart of effective IT operations. If done correctly, change management touches all of IT—including the service desk, operational teams, development, the executive suite, and even non-IT service consumers. This central position makes change management both an opportunity and a challenge.

Change Management Use Cases

Image removed.Probably the best way to understand the “change management opportunity” is to look at some of the use cases affiliated with it. Effective change management can empower a wide range of other initiatives, from lifecycle asset management to DevOps, service impact management, and improved service performance. EMA consultants have estimated that more than 60% of IT service disruptions come from the impacts of changes made across the application infrastructure—and this estimate is conservative compared to some of the other industry estimates I’ve seen. Having good change management processes and technologies in place is also a foundation for better automation, as well as for better optimization of both public and private cloud resources. And the list goes on.

Even the list below, derived in large part from CMDB Systems: Making Change Work in the Age of Cloud and Agile, is a partial one, but it should provide a useful departure point for your planning—as you seek to prioritize the use case(s) most relevant to you.

■ Governance and compliance: Managing change to conform with critical industry, security, and asset-related requirements for compliance, while minimizing change-related disruptions. This, can provide significant financial benefits including OpEx savings, superior service availability, improved security and savings from avoiding the penalty costs incurred when changes are made poorly.

■ Data center consolidation—mergers and acquisitions: Planning new options for data center consolidation is definitely on the rise, and mergers and acquisitions often lead to data center consolidation initiatives. Effective change management can shorten consolidation time, minimize costs, and improve the quality of the outcome.

■ Disaster recovery – Disaster recovery initiatives may be an extension of data center consolidation, or they may be independent. Automating change for disaster recovery is one of the more common drivers for a more systemic approach to change management.

■ The proverbial “move to cloud” – The stunning rise of virtualization and the persistent move to assimilate both internal and public cloud options make change impact management and effective change automation essential.

■ Facilities management and Green IT – This use case requires dynamic insights into both configuration and “performance”-related attributes for configuration items (CIs), both internal to IT (servers, switches, desktops, etc.) and external to traditional IT boundaries (facilities, power, etc.).

■ Optimizing the end-user experience across heterogeneous endpoints – Meeting the challenges of unified endpoint management including mobile endpoints, requires a flexible adoption of change management best practices and automation. But the benefits of doing this can be significant—impacting asset management, security, and financial optimization, while increasing end-user satisfaction with IT services.

Change Management Part 2

Hot Topics

The Latest

For fifteen years, observability lived downstream of everything else. Code shipped, something broke, an engineer went to the dashboards. The job was forensic. The pillars we built, such as logs, metrics, and traces, were designed for that role: tell a human what just happened, fast enough that they can make it stop. That role has quietly ended ...

Hybrid IT has become the standard operating model for enterprises — but that companies are still looking for the right hybrid IT mix, according to the 2026 State of the Data Center Report from CoreSite. After years of cloud migration and hybrid adoption, organizations are shifting their focus from deciding whether to use cloud, colocation or on-premises infrastructure to determining which workloads belong in each environment ...

Pilots are everywhere, stakeholders are seeking results, businesses are pushing for new tools, and IT teams are being asked to make AI secure, reliable, and useful at scale. But as organizations move from testing AI to operationalizing it, many are discovering that the biggest barrier is not the model, the use case, or even the budget. It is the file data foundation within ...

Fast or cheap? For most of my career in engineering, speed and quality sat on opposite ends of a seesaw. The "OR" in "fast or cheap" was non-negotiable. It was expected that pushing for faster releases meant that something in quality would give way. Tightening quality controls meant the schedule slipped. Every engineering leader I know has lived some version of that tradeoff ... The seesaw is starting to level out ...

I have been building enterprise software for more than 20 years ... One thing stays true across all of it: You do not find out your foundation is wrong during the crisis. You find out when the debt comes due. For a lot of organizations, that bill is arriving now. New research ... puts hard numbers on something practitioners have been sensing for a while. The telemetry problem isn't coming. It's already here ...

The rapid growth of AI workloads is pushing traditional log management approaches to their limits, according to The State of Log Management 2026 report from Dynatrace. Modern logs have become critical to understanding, validating, and securing AI-driven decisions, helping organizations ensure reliability, compliance, and performance at scale. However, the volume and complexity of AI telemetry are overwhelming legacy tools ...

For years, secure connectivity has relied on a familiar pattern: route traffic back to centralized gateways, inspect it, and then allow access. This model worked when applications lived in a handful of data centers and users were largely confined to offices. That model is now under strain. Applications are distributed across clouds, users connect from everywhere, and real-time workloads demand performance that centralized inspection points struggle to deliver. As traffic volumes grow and latency expectations shrink, routing everything through a small number of control points has become both a performance bottleneck and a resilience risk. The future of secure connectivity requires a different approach ...

The AI experimentation phase is over, and the private cloud is where enterprise AI workloads are being deployed for security and scale, according to Private Cloud Outlook 2026, a new report from Broadcom ... 2026 marks an acceleration into a full AI tipping point. The shift is being shaped by three forces — costs, complexity, and control — that public cloud environments are increasingly failing to address for production AI at scale. Key findings from the report include ...

44% of organizations have reported an outage in the past year tied to suppressed or ignored alerts, and 78% had at least one incident where no alert was fired at all ... Engineers learned about failures from customers. That gap between what our tools report and what our customers experience is the problem DevOps teams have been quietly solving with GenAI tooling, even as most enterprises continue to run their NOCs on manual alert triage ...

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...